On The Application of Linear Attention in Multimodal Transformers

2026-04-11

AI Summary

This work addresses the scalability limitations of standard multimodal Transformers, whose quadratic computational complexity in attention mechanisms hinders application to long sequences and large-scale data. The study systematically introduces linear attention into large multimodal models for the first time, replacing conventional attention with a linear-complexity alternative. Models based on ViT-S/16, ViT-B/16, and ViT-L/16 architectures are trained on the LAION-400M dataset and evaluated via zero-shot accuracy on ImageNet-21K. Experimental results demonstrate that the proposed approach substantially reduces computational overhead while maintaining performance comparable to standard attention, and exhibits consistent scaling behavior across model sizes. These findings establish linear attention as a viable and efficient paradigm for scalable multimodal representation learning.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Multimodal Transformers serve as the backbone for state-of-the-art vision-language models, yet their quadratic attention complexity remains a critical barrier to scalability. In this work, we investigate the viability of Linear Attention (LA) as a high-efficiency alternative within multimodal frameworks. By integrating LA, we reduce the computational overhead from quadratic to linear relative to sequence length while preserving competitive performance. We evaluate our approach across ViT-S/16, ViT-B/16, and ViT-L/16 architectures trained on the LAION-400M dataset, with validation focused on ImageNet-21K zero-shot accuracy. Our systematic evaluation demonstrates that Linear Attention not only yields significant computational savings but also adheres to the same scaling laws as standard softmax attention. These findings position Linear Attention as a robust, scalable solution for next-generation multimodal Transformers tasked with processing increasingly large and complex datasets.

Problem

• Multimodal Transformers
• Quadratic Attention Complexity
• Scalability
• Linear Attention
• Vision-Language Models

Innovation

• Linear Attention
• Multimodal Transformers
• Computational Efficiency
• Scalability
• Vision-Language Models